Overview

Dataset statistics

Number of variables38
Number of observations1620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory411.5 KiB
Average record size in memory260.1 B

Variable types

Numeric24
Categorical9
DateTime5

Warnings

msno has a high cardinality: 1620 distinct values High cardinality
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 3 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_planHigh correlation
payment_plan_days_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with payment_plan_days_mean and 1 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with payment_plan_days_mean and 1 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with is_autorenew_change_flagHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 3 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_75 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_985 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_25 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
login_freq is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 3 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_plan and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with actual_amount_paid_mean and 2 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with plan_list_price_mean and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with change_in_payment_methods and 3 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
amt_per_day_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 2 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_75 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_985 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
login_freq is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 3 other fieldsHigh correlation
bd is highly correlated with genderHigh correlation
gender is highly correlated with bdHigh correlation
total_payment_channels is highly correlated with total_transactions and 2 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_plan and 1 other fieldsHigh correlation
change_in_plan is highly correlated with change_in_payment_methodsHigh correlation
plan_list_price_mean is highly correlated with actual_amount_paid_mean and 1 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with change_in_payment_methods and 1 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with is_auto_renew_meanHigh correlation
total_transactions is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flagHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_meanHigh correlation
discount_mean is highly correlated with is_discount_mean and 1 other fieldsHigh correlation
is_discount_mean is highly correlated with discount_mean and 1 other fieldsHigh correlation
is_discount_max is highly correlated with discount_mean and 1 other fieldsHigh correlation
amt_per_day_mean is highly correlated with plan_list_price_mean and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
num_25 is highly correlated with num_50High correlation
num_50 is highly correlated with num_25 and 1 other fieldsHigh correlation
num_75 is highly correlated with num_50 and 1 other fieldsHigh correlation
num_985 is highly correlated with num_75High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
registration_duration is highly correlated with total_payment_channels and 2 other fieldsHigh correlation
change_in_plan is highly correlated with amt_per_day_mean and 8 other fieldsHigh correlation
amt_per_day_mean is highly correlated with change_in_plan and 11 other fieldsHigh correlation
city is highly correlated with bd and 3 other fieldsHigh correlation
total_transactions is highly correlated with change_in_plan and 4 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with amt_per_day_mean and 5 other fieldsHigh correlation
is_churn is highly correlated with is_autorenew_change_flag and 2 other fieldsHigh correlation
bd is highly correlated with city and 2 other fieldsHigh correlation
is_auto_renew_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
payment_plan_days_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
change_in_payment_methods is highly correlated with change_in_plan and 2 other fieldsHigh correlation
total_payment_channels is highly correlated with change_in_plan and 4 other fieldsHigh correlation
num_985 is highly correlated with num_50 and 1 other fieldsHigh correlation
is_cancel_change_flag is highly correlated with is_cancel_mean and 1 other fieldsHigh correlation
is_cancel_mean is highly correlated with is_cancel_change_flag and 1 other fieldsHigh correlation
num_50 is highly correlated with num_985 and 3 other fieldsHigh correlation
num_75 is highly correlated with num_985 and 2 other fieldsHigh correlation
is_discount_max is highly correlated with amt_per_day_mean and 3 other fieldsHigh correlation
transaction_date_max is highly correlated with change_in_plan and 10 other fieldsHigh correlation
membership_expire_date_max is highly correlated with amt_per_day_mean and 8 other fieldsHigh correlation
login_freq is highly correlated with total_transactions and 3 other fieldsHigh correlation
gender is highly correlated with city and 1 other fieldsHigh correlation
plan_list_price_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
registration_duration is highly correlated with change_in_plan and 5 other fieldsHigh correlation
is_discount_mean is highly correlated with is_discount_max and 1 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_unq and 1 other fieldsHigh correlation
more_than_30_sum is highly correlated with change_in_plan and 8 other fieldsHigh correlation
actual_amount_paid_mean is highly correlated with amt_per_day_mean and 4 other fieldsHigh correlation
num_unq is highly correlated with num_50 and 3 other fieldsHigh correlation
discount_mean is highly correlated with change_in_plan and 3 other fieldsHigh correlation
registered_via is highly correlated with change_in_plan and 8 other fieldsHigh correlation
num_100 is highly correlated with total_secs and 1 other fieldsHigh correlation
is_autorenew_change_flag is highly correlated with registered_viaHigh correlation
registered_via is highly correlated with is_autorenew_change_flagHigh correlation
membership_duration_mean is highly skewed (γ1 = -37.92216912) Skewed
msno is uniformly distributed Uniform
df_index has unique values Unique
msno has unique values Unique
is_auto_renew_mean has 237 (14.6%) zeros Zeros
is_cancel_mean has 1395 (86.1%) zeros Zeros
discount_mean has 1438 (88.8%) zeros Zeros
is_discount_mean has 1520 (93.8%) zeros Zeros
more_than_30_sum has 210 (13.0%) zeros Zeros
num_25 has 70 (4.3%) zeros Zeros
num_50 has 265 (16.4%) zeros Zeros
num_75 has 410 (25.3%) zeros Zeros
num_985 has 405 (25.0%) zeros Zeros
num_100 has 22 (1.4%) zeros Zeros

Reproduction

Analysis started2023-05-19 11:03:22.350799
Analysis finished2023-05-19 11:04:39.829980
Duration1 minute and 17.48 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1620
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164011.6519
Minimum46
Maximum323899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:39.925133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile18563.4
Q184342
median162677.5
Q3245104.25
95-th percentile308231.75
Maximum323899
Range323853
Interquartile range (IQR)160762.25

Descriptive statistics

Standard deviation93348.64663
Coefficient of variation (CV)0.5691586273
Kurtosis-1.201181249
Mean164011.6519
Median Absolute Deviation (MAD)80650.5
Skewness-0.00572715963
Sum265698876
Variance8713969827
MonotonicityNot monotonic
2023-05-19T11:04:40.058423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
973701
 
0.1%
2595611
 
0.1%
2636951
 
0.1%
185651
 
0.1%
2596791
 
0.1%
2262471
 
0.1%
2833811
 
0.1%
2596421
 
0.1%
2319221
 
0.1%
1528191
 
0.1%
Other values (1610)1610
99.4%
ValueCountFrequency (%)
461
0.1%
3981
0.1%
5981
0.1%
6741
0.1%
7341
0.1%
9541
0.1%
13191
0.1%
14291
0.1%
14691
0.1%
20661
0.1%
ValueCountFrequency (%)
3238991
0.1%
3237441
0.1%
3236501
0.1%
3234561
0.1%
3233621
0.1%
3233441
0.1%
3231691
0.1%
3228211
0.1%
3224651
0.1%
3221561
0.1%

msno
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1620
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
w8s1oZwdZVugLhPzRsSmfFBeniLGowuLi7NgY9FCq44=
 
1
4iUeTCbgS85/LHCudzqRUY9K7uZUaV+XmTLjIYUF23I=
 
1
2+xPFcvkGfuCT8qR0zgaokU5UZCNYMgNb81CNU/P9Go=
 
1
JpWaI65ySCqcBU8OPLZzhthGVmXDl0ELqJmy7U92MHg=
 
1
sJwXclAfmrjPjVl/cqyVKVP7kjERclaUtbs/dhr2Obs=
 
1
Other values (1615)
1615 

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters71280
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1620 ?
Unique (%)100.0%

Sample

1st roww8s1oZwdZVugLhPzRsSmfFBeniLGowuLi7NgY9FCq44=
2nd rowcnncNsMOZE3kiTKQNSBbVCJMdE+PKr56pBYYeHWJ8Zk=
3rd rowaWZDoNfiZtG0IWeC0++ocJXLZORBwDvkYA2rCBA4yAs=
4th rowPh7IvMxuu+1hJp8qYOAy5llX7D97espKsmhEgicBKls=
5th rowcdrXUooJ9G1gXiLFd5NWqQ5HpgmNzl0aeaojPBJ203k=

Common Values

ValueCountFrequency (%)
w8s1oZwdZVugLhPzRsSmfFBeniLGowuLi7NgY9FCq44=1
 
0.1%
4iUeTCbgS85/LHCudzqRUY9K7uZUaV+XmTLjIYUF23I=1
 
0.1%
2+xPFcvkGfuCT8qR0zgaokU5UZCNYMgNb81CNU/P9Go=1
 
0.1%
JpWaI65ySCqcBU8OPLZzhthGVmXDl0ELqJmy7U92MHg=1
 
0.1%
sJwXclAfmrjPjVl/cqyVKVP7kjERclaUtbs/dhr2Obs=1
 
0.1%
DymODQGTZFEJXnF0hn2SuGTYNrSVGvqwFP+K1sGW6og=1
 
0.1%
Ws4XrZKX0bs08dB7pJfDGChW3wY4jf3UMbOmtxsy3/U=1
 
0.1%
LnieTLWsIZjnIVzWh0pY9hAmN1JmX7IZB+rbBsTk+y8=1
 
0.1%
U6+yJJmRLW0bcsNAQ4Iq5bK9kQppsAG55FF3Tvq0dXk=1
 
0.1%
9P1MuH3mqyH3Sd6+lJVmqjQxIITaFwN4bWMkg+QYFFQ=1
 
0.1%
Other values (1610)1610
99.4%

Length

2023-05-19T11:04:40.350761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w8s1ozwdzvuglhpzrssmffbenilgowuli7ngy9fcq441
 
0.1%
4iuetcbgs85/lhcudzqruy9k7uzuav+xmtljiyuf23i1
 
0.1%
2+xpfcvkgfuct8qr0zgaoku5uzcnymgnb81cnu/p9go1
 
0.1%
jpwai65yscqcbu8oplzzhthgvmxdl0elqjmy7u92mhg1
 
0.1%
sjwxclafmrjpjvl/cqyvkvp7kjerclautbs/dhr2obs1
 
0.1%
dymodqgtzfejxnf0hn2sugtynrsvgvqwfp+k1sgw6og1
 
0.1%
ws4xrzkx0bs08db7pjfdgchw3wy4jf3umbomtxsy3/u1
 
0.1%
lnietlwsizjnivzwh0py9hamn1jmx7izb+rbbstk+y81
 
0.1%
u6+yjjmrlw0bcsnaq4iq5bk9kqppsag55ff3tvq0dxk1
 
0.1%
9p1muh3mqyh3sd6+ljvmqjqxiitafwn4bwmkg+qyffq1
 
0.1%
Other values (1610)1610
99.4%

Most occurring characters

ValueCountFrequency (%)
=1620
 
2.3%
81216
 
1.7%
c1215
 
1.7%
o1197
 
1.7%
I1182
 
1.7%
M1181
 
1.7%
01181
 
1.7%
s1164
 
1.6%
g1158
 
1.6%
A1158
 
1.6%
Other values (55)59008
82.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28452
39.9%
Uppercase Letter28227
39.6%
Decimal Number10747
 
15.1%
Math Symbol2759
 
3.9%
Other Punctuation1095
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c1215
 
4.3%
o1197
 
4.2%
s1164
 
4.1%
g1158
 
4.1%
w1157
 
4.1%
k1155
 
4.1%
b1112
 
3.9%
j1110
 
3.9%
r1103
 
3.9%
i1101
 
3.9%
Other values (16)16980
59.7%
Uppercase Letter
ValueCountFrequency (%)
I1182
 
4.2%
M1181
 
4.2%
A1158
 
4.1%
Q1152
 
4.1%
E1145
 
4.1%
Y1139
 
4.0%
U1136
 
4.0%
L1132
 
4.0%
T1114
 
3.9%
R1111
 
3.9%
Other values (16)16777
59.4%
Decimal Number
ValueCountFrequency (%)
81216
11.3%
01181
11.0%
41128
10.5%
91059
9.9%
31052
9.8%
11036
9.6%
71032
9.6%
61029
9.6%
21011
9.4%
51003
9.3%
Math Symbol
ValueCountFrequency (%)
=1620
58.7%
+1139
41.3%
Other Punctuation
ValueCountFrequency (%)
/1095
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56679
79.5%
Common14601
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
c1215
 
2.1%
o1197
 
2.1%
I1182
 
2.1%
M1181
 
2.1%
s1164
 
2.1%
g1158
 
2.0%
A1158
 
2.0%
w1157
 
2.0%
k1155
 
2.0%
Q1152
 
2.0%
Other values (42)44960
79.3%
Common
ValueCountFrequency (%)
=1620
11.1%
81216
 
8.3%
01181
 
8.1%
+1139
 
7.8%
41128
 
7.7%
/1095
 
7.5%
91059
 
7.3%
31052
 
7.2%
11036
 
7.1%
71032
 
7.1%
Other values (3)3043
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII71280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
=1620
 
2.3%
81216
 
1.7%
c1215
 
1.7%
o1197
 
1.7%
I1182
 
1.7%
M1181
 
1.7%
01181
 
1.7%
s1164
 
1.6%
g1158
 
1.6%
A1158
 
1.6%
Other values (55)59008
82.8%

city
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.359876543
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:40.455222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile18
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.857120697
Coefficient of variation (CV)1.343414347
Kurtosis1.761705001
Mean4.359876543
Median Absolute Deviation (MAD)0
Skewness1.70404063
Sum7063
Variance34.30586286
MonotonicityNot monotonic
2023-05-19T11:04:40.556653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11079
66.6%
13101
 
6.2%
5100
 
6.2%
461
 
3.8%
2260
 
3.7%
1547
 
2.9%
637
 
2.3%
1427
 
1.7%
915
 
0.9%
814
 
0.9%
Other values (10)79
 
4.9%
ValueCountFrequency (%)
11079
66.6%
37
 
0.4%
461
 
3.8%
5100
 
6.2%
637
 
2.3%
74
 
0.2%
814
 
0.9%
915
 
0.9%
1012
 
0.7%
1112
 
0.7%
ValueCountFrequency (%)
2260
3.7%
2113
 
0.8%
201
 
0.1%
1812
 
0.7%
175
 
0.3%
161
 
0.1%
1547
2.9%
1427
 
1.7%
13101
6.2%
1212
 
0.7%

bd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7604321
Minimum6.3
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:40.684827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile6.3
Q16.3
median6.3
Q320
95-th percentile35
Maximum59
Range52.7
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation10.77819768
Coefficient of variation (CV)0.8446577354
Kurtosis1.574506315
Mean12.7604321
Median Absolute Deviation (MAD)0
Skewness1.546727874
Sum20671.9
Variance116.1695452
MonotonicityNot monotonic
2023-05-19T11:04:40.819428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6.31123
69.3%
2044
 
2.7%
2236
 
2.2%
2530
 
1.9%
2330
 
1.9%
2727
 
1.7%
2427
 
1.7%
2626
 
1.6%
2125
 
1.5%
1822
 
1.4%
Other values (34)230
 
14.2%
ValueCountFrequency (%)
6.31123
69.3%
142
 
0.1%
152
 
0.1%
165
 
0.3%
1713
 
0.8%
1822
 
1.4%
1921
 
1.3%
2044
 
2.7%
2125
 
1.5%
2236
 
2.2%
ValueCountFrequency (%)
591
 
0.1%
571
 
0.1%
561
 
0.1%
532
0.1%
522
0.1%
513
0.2%
501
 
0.1%
492
0.1%
484
0.2%
474
0.2%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2
1113 
0
268 
1
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

Length

2023-05-19T11:04:41.051290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:41.125484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

Most occurring characters

ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21113
68.7%
0268
 
16.5%
1239
 
14.8%

registered_via
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
7
1051 
4
225 
9
200 
3
130 
13
 
14

Length

Max length2
Median length1
Mean length1.008641975
Min length1

Characters and Unicode

Total characters1634
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
71051
64.9%
4225
 
13.9%
9200
 
12.3%
3130
 
8.0%
1314
 
0.9%

Length

2023-05-19T11:04:41.301593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:41.388898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
71051
64.9%
4225
 
13.9%
9200
 
12.3%
3130
 
8.0%
1314
 
0.9%

Most occurring characters

ValueCountFrequency (%)
71051
64.3%
4225
 
13.8%
9200
 
12.2%
3144
 
8.8%
114
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1634
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
71051
64.3%
4225
 
13.8%
9200
 
12.2%
3144
 
8.8%
114
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common1634
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
71051
64.3%
4225
 
13.8%
9200
 
12.2%
3144
 
8.8%
114
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71051
64.3%
4225
 
13.8%
9200
 
12.2%
3144
 
8.8%
114
 
0.9%
Distinct636
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
Minimum2015-01-01 00:00:00
Maximum2017-01-31 00:00:00
2023-05-19T11:04:41.491862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:04:41.626347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

is_churn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0
1508 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

Length

2023-05-19T11:04:41.870376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:41.940202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

Most occurring characters

ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01508
93.1%
1112
 
6.9%

total_payment_channels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9308642
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:42.011195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q317
95-th percentile23
Maximum31
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.306376705
Coefficient of variation (CV)0.5285766899
Kurtosis-0.7384112912
Mean11.9308642
Median Absolute Deviation (MAD)5
Skewness0.2471306309
Sum19328
Variance39.77038715
MonotonicityNot monotonic
2023-05-19T11:04:42.125894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9106
 
6.5%
897
 
6.0%
1488
 
5.4%
1788
 
5.4%
1386
 
5.3%
1685
 
5.2%
1583
 
5.1%
783
 
5.1%
379
 
4.9%
574
 
4.6%
Other values (21)751
46.4%
ValueCountFrequency (%)
112
 
0.7%
273
4.5%
379
4.9%
470
4.3%
574
4.6%
669
4.3%
783
5.1%
897
6.0%
9106
6.5%
1067
4.1%
ValueCountFrequency (%)
311
 
0.1%
301
 
0.1%
292
 
0.1%
281
 
0.1%
276
 
0.4%
2616
1.0%
2516
1.0%
2431
1.9%
2321
1.3%
2218
1.1%

change_in_payment_methods
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
1428 
2
160 
3
 
28
4
 
3
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

Length

2023-05-19T11:04:42.367373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:42.441148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11428
88.1%
2160
 
9.9%
328
 
1.7%
43
 
0.2%
51
 
0.1%

payment_plan_days_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct142
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.27200278
Minimum7
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:42.548667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile28.69268775
Q130
median30
Q330
95-th percentile33.34313725
Maximum410
Range403
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.00514917
Coefficient of variation (CV)0.6508783889
Kurtosis217.8071299
Mean32.27200278
Median Absolute Deviation (MAD)0
Skewness13.61431953
Sum52280.6445
Variance441.2162918
MonotonicityNot monotonic
2023-05-19T11:04:42.701705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301372
84.7%
28.7515
 
0.9%
28.810
 
0.6%
28.695652177
 
0.4%
26.714285715
 
0.3%
28.636363645
 
0.3%
28.846153855
 
0.3%
295
 
0.3%
454
 
0.2%
37.54
 
0.2%
Other values (132)188
 
11.6%
ValueCountFrequency (%)
71
 
0.1%
12.666666671
 
0.1%
18.142857141
 
0.1%
18.52
0.1%
20.935483871
 
0.1%
22.173913042
0.1%
22.333333334
0.2%
22.81
 
0.1%
23.181818181
 
0.1%
23.428571431
 
0.1%
ValueCountFrequency (%)
4103
0.2%
302.51
 
0.1%
1952
0.1%
1801
 
0.1%
1492
0.1%
147.51
 
0.1%
1351
 
0.1%
132.33333331
 
0.1%
1251
 
0.1%
1202
0.1%

change_in_plan
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
1382 
2
179 
3
 
53
4
 
5
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

Length

2023-05-19T11:04:42.959677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:43.036805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11382
85.3%
2179
 
11.0%
353
 
3.3%
45
 
0.3%
51
 
0.1%

plan_list_price_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct291
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.6361603
Minimum0
Maximum1788
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:43.133278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q199
median112.9381818
Q3149
95-th percentile180
Maximum1788
Range1788
Interquartile range (IQR)50

Descriptive statistics

Standard deviation91.18492909
Coefficient of variation (CV)0.6823372421
Kurtosis208.4362207
Mean133.6361603
Median Absolute Deviation (MAD)13.93818182
Skewness12.61208068
Sum216490.5797
Variance8314.691292
MonotonicityNot monotonic
2023-05-19T11:04:43.279670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99740
45.7%
149333
20.6%
18089
 
5.5%
12926
 
1.6%
10019
 
1.2%
143.049
 
0.6%
142.79166679
 
0.6%
139.68757
 
0.4%
142.52173917
 
0.4%
1396
 
0.4%
Other values (281)375
23.1%
ValueCountFrequency (%)
01
 
0.1%
24.833333331
 
0.1%
74.52
 
0.1%
77.142857141
 
0.1%
87.967741941
 
0.1%
901
 
0.1%
92.81251
 
0.1%
93.260869571
 
0.1%
99740
45.7%
99.066666671
 
0.1%
ValueCountFrequency (%)
17883
0.2%
8942
0.1%
6871
 
0.1%
6561
 
0.1%
629.33333331
 
0.1%
5961
 
0.1%
5821
 
0.1%
5361
 
0.1%
4801
 
0.1%
4772
0.1%

actual_amount_paid_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct287
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.1996603
Minimum0
Maximum1788
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:43.431881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q199
median111.75
Q3149
95-th percentile180
Maximum1788
Range1788
Interquartile range (IQR)50

Descriptive statistics

Standard deviation91.25875507
Coefficient of variation (CV)0.685127536
Kurtosis207.9944239
Mean133.1996603
Median Absolute Deviation (MAD)12.75
Skewness12.59417892
Sum215783.4497
Variance8328.160377
MonotonicityNot monotonic
2023-05-19T11:04:43.568378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99741
45.7%
149356
22.0%
18087
 
5.4%
12928
 
1.7%
10019
 
1.2%
1398
 
0.5%
111.755
 
0.3%
140.72222225
 
0.3%
119.25
 
0.3%
135.66666674
 
0.2%
Other values (277)362
22.3%
ValueCountFrequency (%)
01
0.1%
24.833333331
0.1%
70.714285711
0.1%
74.52
0.1%
772
0.1%
77.142857141
0.1%
84.857142861
0.1%
86.6251
0.1%
87.352941181
0.1%
87.411764711
0.1%
ValueCountFrequency (%)
17883
0.2%
8942
0.1%
6871
 
0.1%
6561
 
0.1%
629.33333331
 
0.1%
5961
 
0.1%
5821
 
0.1%
5361
 
0.1%
4801
 
0.1%
4772
0.1%

is_auto_renew_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8272474253
Minimum0
Maximum1
Zeros237
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:43.735302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3601876146
Coefficient of variation (CV)0.4354049388
Kurtosis1.22819203
Mean0.8272474253
Median Absolute Deviation (MAD)0
Skewness-1.757435963
Sum1340.140829
Variance0.1297351177
MonotonicityNot monotonic
2023-05-19T11:04:43.877957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11255
77.5%
0237
 
14.6%
0.759
 
0.6%
0.87
 
0.4%
0.66666666677
 
0.4%
0.57
 
0.4%
0.85714285716
 
0.4%
0.83333333336
 
0.4%
0.42857142864
 
0.2%
0.8754
 
0.2%
Other values (52)78
 
4.8%
ValueCountFrequency (%)
0237
14.6%
0.11
 
0.1%
0.13333333331
 
0.1%
0.13636363641
 
0.1%
0.15384615381
 
0.1%
0.18181818181
 
0.1%
0.21
 
0.1%
0.21428571431
 
0.1%
0.22222222221
 
0.1%
0.252
 
0.1%
ValueCountFrequency (%)
11255
77.5%
0.95833333333
 
0.2%
0.95454545451
 
0.1%
0.95238095241
 
0.1%
0.94444444443
 
0.2%
0.93753
 
0.2%
0.93333333331
 
0.1%
0.92857142861
 
0.1%
0.92307692311
 
0.1%
0.921
 
0.1%

is_autorenew_change_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
1383 
0
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%

Length

2023-05-19T11:04:44.116253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:44.185346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%

Most occurring characters

ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11383
85.4%
0237
 
14.6%
Distinct632
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
Minimum2015-01-01 00:00:00
Maximum2017-01-31 00:00:00
2023-05-19T11:04:44.268125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:04:44.414735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

transaction_date_max
Date

HIGH CORRELATION

Distinct85
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
Minimum2015-12-15 00:00:00
Maximum2017-02-28 00:00:00
2023-05-19T11:04:44.566814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:04:44.709606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_transactions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9308642
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:44.862170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q317
95-th percentile23
Maximum31
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.306376705
Coefficient of variation (CV)0.5285766899
Kurtosis-0.7384112912
Mean11.9308642
Median Absolute Deviation (MAD)5
Skewness0.2471306309
Sum19328
Variance39.77038715
MonotonicityNot monotonic
2023-05-19T11:04:44.977374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9106
 
6.5%
897
 
6.0%
1488
 
5.4%
1788
 
5.4%
1386
 
5.3%
1685
 
5.2%
1583
 
5.1%
783
 
5.1%
379
 
4.9%
574
 
4.6%
Other values (21)751
46.4%
ValueCountFrequency (%)
112
 
0.7%
273
4.5%
379
4.9%
470
4.3%
574
4.6%
669
4.3%
783
5.1%
897
6.0%
9106
6.5%
1067
4.1%
ValueCountFrequency (%)
311
 
0.1%
301
 
0.1%
292
 
0.1%
281
 
0.1%
276
 
0.4%
2616
1.0%
2516
1.0%
2431
1.9%
2321
1.3%
2218
1.1%

membership_expire_date_max
Date

HIGH CORRELATION

Distinct59
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
Minimum2017-02-01 00:00:00
Maximum2017-03-31 00:00:00
2023-05-19T11:04:45.111950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:04:45.266932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

is_cancel_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01248799314
Minimum0
Maximum0.5
Zeros1395
Zeros (%)86.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:45.401096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.08333333333
Maximum0.5
Range0.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03930297574
Coefficient of variation (CV)3.147261157
Kurtosis43.16468295
Mean0.01248799314
Median Absolute Deviation (MAD)0
Skewness5.353976071
Sum20.23054889
Variance0.001544723902
MonotonicityNot monotonic
2023-05-19T11:04:45.543407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
01395
86.1%
0.0714285714320
 
1.2%
0.0555555555617
 
1.0%
0.062515
 
0.9%
0.114
 
0.9%
0.0909090909112
 
0.7%
0.0769230769212
 
0.7%
0.0526315789512
 
0.7%
0.0666666666711
 
0.7%
0.12510
 
0.6%
Other values (27)102
 
6.3%
ValueCountFrequency (%)
01395
86.1%
0.037037037041
 
0.1%
0.038461538463
 
0.2%
0.047
 
0.4%
0.041666666677
 
0.4%
0.043478260874
 
0.2%
0.045454545454
 
0.2%
0.047619047628
 
0.5%
0.058
 
0.5%
0.0526315789512
 
0.7%
ValueCountFrequency (%)
0.52
 
0.1%
0.33333333333
0.2%
0.28571428571
 
0.1%
0.253
0.2%
0.21428571431
 
0.1%
0.23
0.2%
0.18181818182
 
0.1%
0.17241379311
 
0.1%
0.16666666675
0.3%
0.15384615381
 
0.1%

is_cancel_change_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0
1395 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

Length

2023-05-19T11:04:45.764282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:45.838941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

Most occurring characters

ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01395
86.1%
1225
 
13.9%

discount_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.436500008
Minimum-38.86956522
Maximum90
Zeros1438
Zeros (%)88.8%
Negative91
Negative (%)5.6%
Memory size12.8 KiB
2023-05-19T11:04:45.926124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-38.86956522
5-th percentile-5.382175926
Q10
median0
Q30
95-th percentile5.730769231
Maximum90
Range128.8695652
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.801866399
Coefficient of variation (CV)13.2917899
Kurtosis60.03643088
Mean0.436500008
Median Absolute Deviation (MAD)0
Skewness4.665131468
Sum707.1300129
Variance33.66165371
MonotonicityNot monotonic
2023-05-19T11:04:46.069122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01438
88.8%
-6.20833333311
 
0.7%
-5.9610
 
0.6%
-5.7307692317
 
0.4%
14.96
 
0.4%
-5.3756
 
0.4%
-6.478260875
 
0.3%
8.2777777785
 
0.3%
18.6255
 
0.3%
16.555555564
 
0.2%
Other values (70)123
 
7.6%
ValueCountFrequency (%)
-38.869565222
0.1%
-35.761
0.1%
-32.954545451
0.1%
-27.592592591
0.1%
-24.833333331
0.1%
-22.351
0.1%
-18.6251
0.1%
-15.684210531
0.1%
-13.545454552
0.1%
-12.416666671
0.1%
ValueCountFrequency (%)
901
 
0.1%
601
 
0.1%
501
 
0.1%
42.571428572
0.1%
37.253
0.2%
33.111111113
0.2%
31.928571431
 
0.1%
29.84
0.2%
27.090909091
 
0.1%
24.833333331
 
0.1%

is_discount_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008801214038
Minimum0
Maximum1
Zeros1520
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:46.220577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.04773809524
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04973378859
Coefficient of variation (CV)5.650787309
Kurtosis144.2724941
Mean0.008801214038
Median Absolute Deviation (MAD)0
Skewness10.17689583
Sum14.25796674
Variance0.002473449728
MonotonicityNot monotonic
2023-05-19T11:04:46.368739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
01520
93.8%
0.043478260876
 
0.4%
0.16
 
0.4%
0.1255
 
0.3%
0.055555555565
 
0.3%
0.11111111114
 
0.2%
0.090909090914
 
0.2%
0.24
 
0.2%
0.254
 
0.2%
0.052631578954
 
0.2%
Other values (36)58
 
3.6%
ValueCountFrequency (%)
01520
93.8%
0.033333333331
 
0.1%
0.037037037041
 
0.1%
0.038461538464
 
0.2%
0.041
 
0.1%
0.041666666673
 
0.2%
0.043478260876
 
0.4%
0.045454545452
 
0.1%
0.047619047621
 
0.1%
0.053
 
0.2%
ValueCountFrequency (%)
11
0.1%
0.63636363642
0.1%
0.51
0.1%
0.40909090911
0.1%
0.36363636361
0.1%
0.33333333331
0.1%
0.30769230771
0.1%
0.29166666671
0.1%
0.28571428572
0.1%
0.281
0.1%

is_discount_max
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0
1520 
1
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

Length

2023-05-19T11:04:46.598446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-05-19T11:04:46.674240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

Most occurring characters

ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01520
93.8%
1100
 
6.2%

amt_per_day_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct321
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.081865522
Minimum0
Maximum6
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:46.758714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q13.3
median3.526573751
Q34.966666667
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1.666666667

Descriptive statistics

Standard deviation0.9029994755
Coefficient of variation (CV)0.2212222501
Kurtosis-0.6878973932
Mean4.081865522
Median Absolute Deviation (MAD)0.3801722803
Skewness0.4313038117
Sum6612.622146
Variance0.8154080527
MonotonicityNot monotonic
2023-05-19T11:04:46.896210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3652
40.2%
4.966666667296
18.3%
3.389
 
5.5%
687
 
5.4%
4.326
 
1.6%
3.33333333319
 
1.2%
4.6907407417
 
0.4%
3.9733333336
 
0.4%
4.7686
 
0.4%
4.7597222226
 
0.4%
Other values (311)426
26.3%
ValueCountFrequency (%)
01
0.1%
0.82777777781
0.1%
1.5039173791
0.1%
2.2923076921
0.1%
2.3571428571
0.1%
2.4833333332
0.1%
2.5647696481
0.1%
2.5666666672
0.1%
2.5714285711
0.1%
2.8285714291
0.1%
ValueCountFrequency (%)
687
5.4%
5.92
 
0.1%
5.8909090911
 
0.1%
5.8888888891
 
0.1%
5.8547619051
 
0.1%
5.8509977831
 
0.1%
5.8461538461
 
0.1%
5.8427350431
 
0.1%
5.8333333331
 
0.1%
5.83
 
0.2%

membership_duration_mean
Real number (ℝ)

SKEWED

Distinct459
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.31085938
Minimum-4273.25
Maximum414
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size12.8 KiB
2023-05-19T11:04:47.052361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4273.25
5-th percentile27.24903846
Q129.98
median30.3125
Q330.42105263
95-th percentile42.20666667
Maximum414
Range4687.25
Interquartile range (IQR)0.4410526316

Descriptive statistics

Standard deviation109.042384
Coefficient of variation (CV)3.597469232
Kurtosis1501.452087
Mean30.31085938
Median Absolute Deviation (MAD)0.2430555556
Skewness-37.92216912
Sum49103.5922
Variance11890.24151
MonotonicityNot monotonic
2023-05-19T11:04:47.188084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30135
 
8.3%
30.3333333383
 
5.1%
30.37560
 
3.7%
30.453
 
3.3%
2946
 
2.8%
30.2857142943
 
2.7%
30.4117647136
 
2.2%
30.3076923133
 
2.0%
30.2533
 
2.0%
30.4210526329
 
1.8%
Other values (449)1069
66.0%
ValueCountFrequency (%)
-4273.251
0.1%
71
0.1%
12.3751
0.1%
151
0.1%
16.51
0.1%
17.251
0.1%
17.51
0.1%
182
0.1%
18.333333331
0.1%
18.51
0.1%
ValueCountFrequency (%)
4141
0.1%
4131
0.1%
4111
0.1%
197.66666671
0.1%
196.51
0.1%
1951
0.1%
1891
0.1%
1801
0.1%
150.33333331
0.1%
147.33333331
0.1%

more_than_30_sum
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.716049383
Minimum0
Maximum30
Zeros210
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:47.321215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile13
Maximum30
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.297850039
Coefficient of variation (CV)0.7518916915
Kurtosis0.2541138802
Mean5.716049383
Median Absolute Deviation (MAD)4
Skewness0.5507513391
Sum9260
Variance18.47151496
MonotonicityNot monotonic
2023-05-19T11:04:47.433630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0210
13.0%
1152
9.4%
5137
8.5%
9133
8.2%
7132
8.1%
2123
 
7.6%
10117
 
7.2%
6108
 
6.7%
4101
 
6.2%
398
 
6.0%
Other values (14)309
19.1%
ValueCountFrequency (%)
0210
13.0%
1152
9.4%
2123
7.6%
398
6.0%
4101
6.2%
5137
8.5%
6108
6.7%
7132
8.1%
891
5.6%
9133
8.2%
ValueCountFrequency (%)
301
 
0.1%
261
 
0.1%
211
 
0.1%
201
 
0.1%
192
 
0.1%
189
 
0.6%
172
 
0.1%
166
 
0.4%
155
 
0.3%
1435
2.2%

num_25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1290
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.079500914
Minimum0
Maximum5.003946304
Zeros70
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:47.561944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1149469078
Q10.5556846708
median0.9921378791
Q31.484854251
95-th percentile2.387628365
Maximum5.003946304
Range5.003946304
Interquartile range (IQR)0.9291695803

Descriptive statistics

Standard deviation0.7032536268
Coefficient of variation (CV)0.6514618397
Kurtosis1.182220221
Mean1.079500914
Median Absolute Deviation (MAD)0.4566862583
Skewness0.8732081056
Sum1748.791504
Variance0.4945656955
MonotonicityNot monotonic
2023-05-19T11:04:47.708859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070
 
4.3%
0.173286795628
 
1.7%
0.346573591222
 
1.4%
0.274653077115
 
0.9%
0.402359485613
 
0.8%
0.447939872712
 
0.7%
0.693147182510
 
0.6%
0.7945134649
 
0.6%
0.62122666849
 
0.6%
0.23104906088
 
0.5%
Other values (1280)1424
87.9%
ValueCountFrequency (%)
070
4.3%
0.057762265212
 
0.1%
0.077016353611
 
0.1%
0.086643397815
 
0.3%
0.091551028191
 
0.1%
0.10058987141
 
0.1%
0.10397207741
 
0.1%
0.11552453045
 
0.3%
0.12043216081
 
0.1%
0.1214713381
 
0.1%
ValueCountFrequency (%)
5.0039463041
0.1%
4.1405954361
0.1%
3.8676047331
0.1%
3.8127706051
0.1%
3.7715411191
0.1%
3.7365491391
0.1%
3.5278499131
0.1%
3.5228881841
0.1%
3.4562621121
0.1%
3.4238924981
0.1%

num_50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct823
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3590388298
Minimum0
Maximum2.708050251
Zeros265
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:47.854255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1155245304
median0.2746530771
Q30.5198603868
95-th percentile1.047928405
Maximum2.708050251
Range2.708050251
Interquartile range (IQR)0.4043358564

Descriptive statistics

Standard deviation0.3483375907
Coefficient of variation (CV)0.970194757
Kurtosis4.850337982
Mean0.3590388298
Median Absolute Deviation (MAD)0.1880096793
Skewness1.749609113
Sum581.6428833
Variance0.1213390753
MonotonicityNot monotonic
2023-05-19T11:04:47.998579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0265
 
16.4%
0.173286795681
 
5.0%
0.0866433978133
 
2.0%
0.346573591230
 
1.9%
0.115524530422
 
1.4%
0.231049060821
 
1.3%
0.274653077119
 
1.2%
0.0577622652118
 
1.1%
0.519860386813
 
0.8%
0.447939872712
 
0.7%
Other values (813)1106
68.3%
ValueCountFrequency (%)
0265
16.4%
0.024755256253
 
0.2%
0.027465308091
 
0.1%
0.02888113263
 
0.2%
0.034657359125
 
0.3%
0.03850817681
 
0.1%
0.04332169894
 
0.2%
0.04577551412
 
0.1%
0.046209812161
 
0.1%
0.047260034831
 
0.1%
ValueCountFrequency (%)
2.7080502511
0.1%
2.3978953361
0.1%
2.2376573091
0.1%
2.2154083251
0.1%
2.1383330821
0.1%
2.0794415471
0.1%
2.0501258371
0.1%
1.887653471
0.1%
1.8212052581
0.1%
1.8180996181
0.1%

num_75
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct601
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2241915166
Minimum0
Maximum2.564949274
Zeros410
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:48.151742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1732867956
Q30.3157505095
95-th percentile0.6932374895
Maximum2.564949274
Range2.564949274
Interquartile range (IQR)0.3157505095

Descriptive statistics

Standard deviation0.2568334043
Coefficient of variation (CV)1.145598173
Kurtosis10.52245426
Mean0.2241915166
Median Absolute Deviation (MAD)0.1490656137
Skewness2.406011105
Sum363.1902466
Variance0.06596340239
MonotonicityNot monotonic
2023-05-19T11:04:48.291838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0410
25.3%
0.173286795677
 
4.8%
0.0866433978146
 
2.8%
0.115524530438
 
2.3%
0.0577622652129
 
1.8%
0.346573591227
 
1.7%
0.274653077125
 
1.5%
0.231049060825
 
1.5%
0.0915510281914
 
0.9%
0.138629436513
 
0.8%
Other values (591)916
56.5%
ValueCountFrequency (%)
0410
25.3%
0.01444056631
 
0.1%
0.01925408841
 
0.1%
0.021660849451
 
0.1%
0.024755256255
 
0.3%
0.028881132610
 
0.6%
0.034657359129
 
0.6%
0.03850817683
 
0.2%
0.039236154411
 
0.1%
0.043321698911
 
0.7%
ValueCountFrequency (%)
2.5649492741
0.1%
2.3025851251
0.1%
1.7668291331
0.1%
1.7631802561
0.1%
1.4656542541
0.1%
1.3862943652
0.1%
1.3701597451
0.1%
1.3052765131
0.1%
1.2972735171
0.1%
1.2926210171
0.1%

num_985
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct607
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2246976197
Minimum0
Maximum2.079441547
Zeros405
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:48.438678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01083042473
median0.1732867956
Q30.3226000965
95-th percentile0.7324753463
Maximum2.079441547
Range2.079441547
Interquartile range (IQR)0.3117696717

Descriptive statistics

Standard deviation0.2528685033
Coefficient of variation (CV)1.12537241
Kurtosis5.4888978
Mean0.2246976197
Median Absolute Deviation (MAD)0.1493133008
Skewness1.953810692
Sum364.0101318
Variance0.06394248456
MonotonicityNot monotonic
2023-05-19T11:04:48.581949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0405
25.0%
0.173286795687
 
5.4%
0.0866433978142
 
2.6%
0.0577622652135
 
2.2%
0.231049060829
 
1.8%
0.115524530426
 
1.6%
0.274653077124
 
1.5%
0.346573591223
 
1.4%
0.223969936418
 
1.1%
0.447939872715
 
0.9%
Other values (597)916
56.5%
ValueCountFrequency (%)
0405
25.0%
0.01444056631
 
0.1%
0.021660849451
 
0.1%
0.024755256253
 
0.2%
0.02888113265
 
0.3%
0.033007007092
 
0.1%
0.0346573591211
 
0.7%
0.03850817683
 
0.2%
0.039236154412
 
0.1%
0.040721807631
 
0.1%
ValueCountFrequency (%)
2.0794415471
0.1%
1.7847168451
0.1%
1.425945641
0.1%
1.3862943651
0.1%
1.3602486851
0.1%
1.3195286991
0.1%
1.3164687161
0.1%
1.312673331
0.1%
1.2982392311
0.1%
1.2834913731
0.1%

num_100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1553
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.400013208
Minimum0
Maximum5.924189091
Zeros22
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:48.721299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8953939259
Q11.886082977
median2.418399572
Q32.946542084
95-th percentile3.805382395
Maximum5.924189091
Range5.924189091
Interquartile range (IQR)1.060459107

Descriptive statistics

Standard deviation0.8693374395
Coefficient of variation (CV)0.3622219265
Kurtosis0.5379921794
Mean2.400013208
Median Absolute Deviation (MAD)0.5311615467
Skewness-0.05542365834
Sum3888.021484
Variance0.7557476163
MonotonicityNot monotonic
2023-05-19T11:04:48.866407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
1.4%
0.69314718256
 
0.4%
0.72259294993
 
0.2%
1.4715260273
 
0.2%
1.5556440353
 
0.2%
0.89587974553
 
0.2%
0.86643397813
 
0.2%
0.27465307713
 
0.2%
1.0235861542
 
0.1%
0.51986038682
 
0.1%
Other values (1543)1570
96.9%
ValueCountFrequency (%)
022
1.4%
0.23104906081
 
0.1%
0.27465307713
 
0.2%
0.30806541441
 
0.1%
0.34657359121
 
0.1%
0.36620411282
 
0.1%
0.40235948561
 
0.1%
0.43052789571
 
0.1%
0.44793987272
 
0.1%
0.48647755382
 
0.1%
ValueCountFrequency (%)
5.9241890911
0.1%
5.4813704491
0.1%
5.2248387341
0.1%
5.0985484121
0.1%
4.9545960431
0.1%
4.9037647251
0.1%
4.8901138311
0.1%
4.8859310151
0.1%
4.8804221151
0.1%
4.844253541
0.1%

num_unq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1579
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.621389151
Minimum0
Maximum5.257495403
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-05-19T11:04:49.005845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.314168793
Q12.181361258
median2.659072638
Q33.083636224
95-th percentile3.811152709
Maximum5.257495403
Range5.257495403
Interquartile range (IQR)0.9022749662

Descriptive statistics

Standard deviation0.7503094673
Coefficient of variation (CV)0.2862258852
Kurtosis0.6455993056
Mean2.621389151
Median Absolute Deviation (MAD)0.4508309364
Skewness-0.2068370581
Sum4246.650391
Variance0.5629643202
MonotonicityNot monotonic
2023-05-19T11:04:49.158511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69314718257
 
0.4%
05
 
0.3%
2.0593698024
 
0.2%
1.0986123093
 
0.2%
0.89587974553
 
0.2%
1.8180996183
 
0.2%
0.59725314383
 
0.2%
1.0593513253
 
0.2%
1.3701597452
 
0.1%
0.74893307692
 
0.1%
Other values (1569)1585
97.8%
ValueCountFrequency (%)
05
0.3%
0.17328679561
 
0.1%
0.34657359122
 
0.1%
0.54930615432
 
0.1%
0.59725314383
0.2%
0.62122666841
 
0.1%
0.67701256281
 
0.1%
0.69314718257
0.4%
0.74893307692
 
0.1%
0.77394181491
 
0.1%
ValueCountFrequency (%)
5.2574954031
0.1%
4.9437513351
0.1%
4.8475904461
0.1%
4.7948241231
0.1%
4.7553663251
0.1%
4.7383866311
0.1%
4.7239780431
0.1%
4.697792531
0.1%
4.6250410081
0.1%
4.534298421
0.1%

total_secs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct583
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0078125
Minimum0.9223632812
Maximum10.890625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-05-19T11:04:49.299944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.9223632812
5-th percentile6.464453125
Q17.549804688
median8.0625
Q38.5625
95-th percentile9.351953125
Maximum10.890625
Range9.968261719
Interquartile range (IQR)1.012695312

Descriptive statistics

Standard deviation0.9106445312
Coefficient of variation (CV)0.1137084961
Kurtosis4.74609375
Mean8.0078125
Median Absolute Deviation (MAD)0.5078125
Skewness-1.012695312
Sum12976
Variance0.8291015625
MonotonicityNot monotonic
2023-05-19T11:04:49.443995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.070312511
 
0.7%
8.12511
 
0.7%
8.10937510
 
0.6%
8.26562510
 
0.6%
8.210937510
 
0.6%
8.0312510
 
0.6%
8.164062510
 
0.6%
8.35156259
 
0.6%
8.2343759
 
0.6%
8.59
 
0.6%
Other values (573)1521
93.9%
ValueCountFrequency (%)
0.92236328121
0.1%
2.89843751
0.1%
3.550781251
0.1%
3.69531251
0.1%
3.707031251
0.1%
3.74218751
0.1%
3.777343751
0.1%
3.996093751
0.1%
4.43751
0.1%
4.6251
0.1%
ValueCountFrequency (%)
10.8906251
0.1%
10.63281251
0.1%
10.60156251
0.1%
10.43751
0.1%
10.4218751
0.1%
10.406251
0.1%
10.3751
0.1%
10.3281251
0.1%
10.2031251
0.1%
10.18751
0.1%

login_freq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.08271605
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:49.593914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median12
Q321
95-th percentile39
Maximum60
Range59
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.50644785
Coefficient of variation (CV)0.7628896424
Kurtosis0.4466703241
Mean15.08271605
Median Absolute Deviation (MAD)8
Skewness1.024195927
Sum24434
Variance132.3983422
MonotonicityNot monotonic
2023-05-19T11:04:49.732488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4298
18.4%
12190
11.7%
8177
10.9%
20110
 
6.8%
16106
 
6.5%
2493
 
5.7%
2873
 
4.5%
370
 
4.3%
3254
 
3.3%
649
 
3.0%
Other values (39)400
24.7%
ValueCountFrequency (%)
123
 
1.4%
224
 
1.5%
370
 
4.3%
4298
18.4%
515
 
0.9%
649
 
3.0%
75
 
0.3%
8177
10.9%
932
 
2.0%
109
 
0.6%
ValueCountFrequency (%)
601
 
0.1%
561
 
0.1%
529
 
0.6%
4813
0.8%
462
 
0.1%
452
 
0.1%
4423
1.4%
431
 
0.1%
421
 
0.1%
411
 
0.1%
Distinct275
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
Minimum2015-02-27 00:00:00
Maximum2017-02-28 00:00:00
2023-05-19T11:04:49.884606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:04:50.017435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

registration_duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct454
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.6481481
Minimum14
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2023-05-19T11:04:50.170702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile89
Q1243
median408.5
Q3547
95-th percentile745
Maximum809
Range795
Interquartile range (IQR)304

Descriptive statistics

Standard deviation195.3883345
Coefficient of variation (CV)0.4840560657
Kurtosis-0.8639791412
Mean403.6481481
Median Absolute Deviation (MAD)151
Skewness0.0512464614
Sum653910
Variance38176.60126
MonotonicityNot monotonic
2023-05-19T11:04:50.318333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51742
 
2.6%
39435
 
2.2%
57833
 
2.0%
39331
 
1.9%
60931
 
1.9%
27230
 
1.9%
24229
 
1.8%
8929
 
1.8%
42528
 
1.7%
45528
 
1.7%
Other values (444)1304
80.5%
ValueCountFrequency (%)
141
 
0.1%
301
 
0.1%
341
 
0.1%
351
 
0.1%
361
 
0.1%
521
 
0.1%
5820
1.2%
595
 
0.3%
601
 
0.1%
614
 
0.2%
ValueCountFrequency (%)
8091
 
0.1%
8071
 
0.1%
8051
 
0.1%
8012
0.1%
7991
 
0.1%
7982
0.1%
7972
0.1%
7961
 
0.1%
7951
 
0.1%
7933
0.2%

Interactions

2023-05-19T11:03:25.167822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.286366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.433770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.545921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.665803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.794920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:25.910661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.026994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.151083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.270891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.383140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.508497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.626852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.739733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.859975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:26.981293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.090903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.210810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.329304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.439169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.559554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.677796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.786615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:27.906803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.037436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.179874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.346759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.485991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.633989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.789068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:28.930831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.087192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.227554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.372561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.521878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.661048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-19T11:03:29.816229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-19T11:04:37.391881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-19T11:04:50.478387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-19T11:04:50.794487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-19T11:04:51.113552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-19T11:04:51.438273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-05-19T11:04:51.738877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-05-19T11:04:38.648947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-19T11:04:39.624851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexmsnocitybdgenderregistered_viaregistration_init_timeis_churntotal_payment_channelschange_in_payment_methodspayment_plan_days_meanchange_in_planplan_list_price_meanactual_amount_paid_meanis_auto_renew_meanis_autorenew_change_flagtransaction_date_mintransaction_date_maxtotal_transactionsmembership_expire_date_maxis_cancel_meanis_cancel_change_flagdiscount_meanis_discount_meanis_discount_maxamt_per_day_meanmembership_duration_meanmore_than_30_sumnum_25num_50num_75num_985num_100num_unqtotal_secslogin_freqlast_loginregistration_duration
097370w8s1oZwdZVugLhPzRsSmfFBeniLGowuLi7NgY9FCq44=136.3042016-01-3012218.500000290.00000090.0000000.002016-11-162017-01-1522017-02-140.00000000.00.003.00000018.50000000.1732870.0000000.0000000.0000003.2993683.3533298.92968842017-02-11381
1100926cnncNsMOZE3kiTKQNSBbVCJMdE+PKr56pBYYeHWJ8Zk=630.0042016-02-06013130.0000001149.000000149.0000001.012016-02-122017-02-12132017-03-110.00000000.00.004.96666729.30769200.1831020.0000000.0000000.0000003.3352153.3473968.75781262016-10-05399
266560aWZDoNfiZtG0IWeC0++ocJXLZORBwDvkYA2rCBA4yAs=16.3272015-12-20015130.000000199.00000099.0000001.012015-12-202017-02-20152017-03-200.00000000.00.003.30000030.40000092.2948160.6135350.4588580.3146092.7769713.1253208.390625362017-02-28456
3316477Ph7IvMxuu+1hJp8qYOAy5llX7D97espKsmhEgicBKls=16.3272015-10-04019130.0000001123.210526123.2105261.012015-10-042017-02-24192017-03-240.05263210.00.004.10701828.789474101.4656360.6364440.5282730.5311892.7706343.0166278.390625282017-01-04537
4293127cdrXUooJ9G1gXiLFd5NWqQ5HpgmNzl0aeaojPBJ203k=16.3272016-01-3109130.000000199.00000099.0000001.012016-01-312017-02-2892017-03-310.00000000.00.003.30000030.22222252.3851411.3772371.1751021.1345501.9699443.0760428.007812282017-02-26425
523642fa9aoepY5IaSL3fZW92105ar3bmT6kea69Qg+7osIfM=524.0072015-04-23024130.0000001139.000000139.0000001.012015-04-232017-02-21242017-03-210.04166710.00.004.63333329.125000130.7324010.1732870.1685670.0962702.5977372.5265088.273438362016-12-28698
6306400U03kCv151wiDiVUUAzAQp3IlXuyNQRs47zJxC3FgSy0=16.3292015-01-17014226.7142862127.714286127.7142860.002015-06-072017-01-13142017-02-150.00000000.00.004.25714332.14285751.6706391.2161701.1291970.9053190.3080652.4119686.59765692017-02-12760
7160211ELGZ1U3Bcg51pCDH17M5EPQnWkvwnTVktorG+Dks2gE=1124.0192016-12-0703130.0000001149.000000149.0000001.012016-12-112017-02-1132017-03-100.00000000.00.004.96666729.00000000.1732870.0866430.0866430.0866432.6656092.6611568.24218882017-02-1493
8225006Nv3He2E4qnnPeC1TJulbW84VQouU1S/3Vxuqkhk5Efo=16.3272015-07-17020130.000000199.00000099.0000001.012015-07-172017-02-16202017-03-160.00000000.00.003.30000030.400000111.8268801.2688880.3662040.2310490.9985772.5317967.19140632016-09-26608
9195028YNfyqKWvZ4I8n8uXxQzLr2mSIAoGT1ZFfUrnSH75juU=16.32132016-11-2004130.0000001100.000000100.0000001.012016-11-202017-02-2042017-03-190.00000000.00.003.33333329.00000000.7675280.0000000.0000000.0000001.5513201.4769396.66015632016-11-25119

Last rows

df_indexmsnocitybdgenderregistered_viaregistration_init_timeis_churntotal_payment_channelschange_in_payment_methodspayment_plan_days_meanchange_in_planplan_list_price_meanactual_amount_paid_meanis_auto_renew_meanis_autorenew_change_flagtransaction_date_mintransaction_date_maxtotal_transactionsmembership_expire_date_maxis_cancel_meanis_cancel_change_flagdiscount_meanis_discount_meanis_discount_maxamt_per_day_meanmembership_duration_meanmore_than_30_sumnum_25num_50num_75num_985num_100num_unqtotal_secslogin_freqlast_loginregistration_duration
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